# Hyperbolic Relevance Matching for Neural Keyphrase Extraction arXiv: https://arxiv.org/abs/2205.02047 ยท HTML: https://mlsanity.com/paper/2205.02047/ ## Key facts - Cited by 7 papers in the map (data through 14 Oct 2026). - Builds on 31 of its 55 references that are in the map. - First posted on arXiv 4 May 2022. ## Builds on (most cited first, 30 of 31) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019) - [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019) - [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019) - [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018) - [Poincar'e Embeddings for Learning Hierarchical Representations](https://mlsanity.com/paper/1705.08039/) (2017) - [Hyperbolic Neural Networks](https://mlsanity.com/paper/1805.09112/) (2018) - [Hyperbolic Image Embeddings](https://mlsanity.com/paper/1904.02239/) (2019) - [Representation Tradeoffs for Hyperbolic Embeddings](https://mlsanity.com/paper/1804.03329/) (2018) - [Extractive Summarization as Text Matching](https://mlsanity.com/paper/2004.08795/) (2020) - [Poincar'e GloVe: Hyperbolic Word Embeddings](https://mlsanity.com/paper/1810.06546/) (2018) - [Hyperbolic Attention Networks](https://mlsanity.com/paper/1805.09786/) (2018) - [Bilateral Multi-Perspective Matching for Natural Language Sentences](https://mlsanity.com/paper/1702.03814/) (2017) - [Continuous Hierarchical Representations with Poincar'e Variational Auto-Encoders](https://mlsanity.com/paper/1901.06033/) (2019) - [Deep Keyphrase Generation](https://mlsanity.com/paper/1704.06879/) (2017) - [Embedding Text in Hyperbolic Spaces](https://mlsanity.com/paper/1806.04313/) (2018) - [Probing BERT in Hyperbolic Spaces](https://mlsanity.com/paper/2104.03869/) (2021) - [What's so special about BERT's layers? A closer look at the NLP pipeline in monolingual and multilingual models](https://mlsanity.com/paper/2004.06499/) (2020) - [An Integrated Approach for Keyphrase Generation via Exploring the Power of Retrieval and Extraction](https://mlsanity.com/paper/1904.03454/) (2019) - [Open Domain Web Keyphrase Extraction Beyond Language Modeling](https://mlsanity.com/paper/1911.02671/) (2019) - [HyperText: Endowing FastText with Hyperbolic Geometry](https://mlsanity.com/paper/2010.16143/) (2020) - [Select, Extract and Generate: Neural Keyphrase Generation with Layer-wise Coverage Attention](https://mlsanity.com/paper/2008.01739/) (2020) - [DivGraphPointer: A Graph Pointer Network for Extracting Diverse Keyphrases](https://mlsanity.com/paper/1905.07689/) (2019) - [Importance Estimation from Multiple Perspectives for Keyphrase Extraction](https://mlsanity.com/paper/2110.09749/) (2021) - [UniKeyphrase: A Unified Extraction and Generation Framework for Keyphrase Prediction](https://mlsanity.com/paper/2106.04847/) (2021) - [APo-VAE: Text Generation in Hyperbolic Space](https://mlsanity.com/paper/2005.00054/) (2020) - [Syntactic Perturbations Reveal Representational Correlates of Hierarchical Phrase Structure in Pretrained Language Models](https://mlsanity.com/paper/2104.07578/) (2021) - [Parsing All: Syntax and Semantics, Dependencies and Spans](https://mlsanity.com/paper/1908.11522/) (2019) - [Keyphrase Prediction With Pre-trained Language Model](https://mlsanity.com/paper/2004.10462/) (2020) - [Keyphrase Extraction with Span-based Feature Representations](https://mlsanity.com/paper/2002.05407/) (2020) ## Built on by (most cited first, 7 of 7) - [Is ChatGPT A Good Keyphrase Generator? A Preliminary Study](https://mlsanity.com/paper/2303.13001/) (2023) - [Klein Model for Hyperbolic Neural Networks](https://mlsanity.com/paper/2410.16813/) (2024) - [Probing Pretrained Language Models with Hierarchy Properties](https://mlsanity.com/paper/2312.09670/) (2023) - [Why are hyperbolic neural networks effective? A study on hierarchical representation capability](https://mlsanity.com/paper/2402.02478/) (2024) - [Exploring Fine-tuned Generative Models for Keyphrase Selection: A Case Study for Russian](https://mlsanity.com/paper/2409.10640/) (2024) - [LongKey: Keyphrase Extraction for Long Documents](https://mlsanity.com/paper/2411.17863/) (2024) - [Meaning in Order, Order in Meaning: Semantic R-precision for Keyphrase Evaluation](https://mlsanity.com/paper/2606.07057/) (2026) ## Abstract (arXiv) Keyphrase extraction is a fundamental task in natural language processing and information retrieval that aims to extract a set of phrases with important information from a source document. Identifying important keyphrase is the central component of the keyphrase extraction task, and its main challenge is how to represent information comprehensively and discriminate importance accurately. In this paper, to address these issues, we design a new hyperbolic matching model (HyperMatch) to represent phrases and documents in the same hyperbolic space and explicitly estimate the phrase-document relevance via the Poincar'e distance as the important score of each phrase. Specifically, to capture the hierarchical syntactic and semantic structure information, HyperMatch takes advantage of the hidden representations in multiple layers of RoBERTa and integrates them as the word embeddings via an adaptive mixing layer. Meanwhile, considering the hierarchical structure hidden in the document, HyperMatch embeds both phrases and documents in the same hyperbolic space via a hyperbolic phrase encoder and a hyperbolic document encoder. This strategy can further enhance the estimation of phrase-document relevance due to the good properties of hyperbolic space. In this setting, the keyphrase extraction can be taken as a matching problem and effectively implemented by minimizing a hyperbolic margin-based triplet loss. Extensive experiments are conducted on six benchmarks and demonstrate that HyperMatch outperforms the state-of-the-art baselines.